AI in Finance​

The financial services industry is one of the leaders in adopting new AI and Machine learning technologies. The high volume of well organized, accurate, historical and quantitative nature of the data means the ‘raw material’ for tackling business challenges with AI is abundant and readily available. The industry is teeming with opportunities for improving operations, prediction accuracy and profitability.​​

Companies not already investing in AI solutions not only risk being left behind by competitors but also are likely to struggle through new upcoming rules and regulations regarding cybersecurity. AI-powered security systems will become a necessity to catch on with the exponentially growing complexity of the financial sector.

With the wealth of data available means we’ve only begun to scratch the surface and AI will continue to unlock more value for financial enterprises and their customer.

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Applications of AI in the Finance Industry

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Time Series Prediction

Brainpool developed a bespoke machine learning pipeline Forstack, which stacks various models in real time to increase prediction accuracy.
Benefits:
• Better prediction accuracy than single model predictions
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Automated Due Diligence

AI can be used to review agreements, financial documents and contracts to accelerate the due diligence process in M&A and Private Equity
Benefits:
• Less possibility for human error
• Faster turnaround times
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Fraud Detection and Risk Management

Build bespoke risk models by identifying key data features and nonlinear patterns in large datasets, and early warning systems that automate reporting, portfolio monitoring and contingency plans.
Benefits:
• Risk mitigation
• Fraud prevention
• Continuous system improvements
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Portfolio Management

Machine learning algorithms calibrate investment decisions according to the investors goal and market fluctuations; with the aim of finding the best outcome against the set objectives.
Benefits:
• Better insights
• Smarter decisions
• Increased profitability

Benefits of AI in finance

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Better and faster insights

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Increased
profitability

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Lower
risk

Case Study: ML-powered time-series prediction for FX market

Client: Japanese Tier-1 Investment Bank
The Challenge

Accurately forecasting currency movements in the short-term future and make data-driven decisions.

The Solution

 Brainpool developed a Predictive Machine Learning System which used historical FX data to make currency fluctuations predictions.
Brainpool gained a thorough understanding of the client's data structure to aggregate various data sources, which were used to uncover patterns, trends and identify key factors influencing the value of a given currency.

Results

Using large quantities of training data and making incremental changes, we were able to successfully develop and algorithm which increased the accuracy of the clients FX forecasting from 52% to 56%.